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franciscothebrady / R4SS

R statistical programming language for Social Sciences
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Nail down table of contents #1

Open franciscothebrady opened 4 years ago

franciscothebrady commented 4 years ago
  1. intro: what is the aim of the book
    • Why do social science practitioners need programming and statistical skills
    • etc
  2. introduction to R: beginning steps
    • begin with case study of social science data project
    • introduction to data types in R
    • extracting data stored in different formats (pdfs, csv, etc)
  3. statistical concepts for effective for social science research
    • descriptive statistics: what we know
    • correlation - connecting the dots
    • causal inference - establishing a direction
    • external validity - controlling for confounders (maybe move to later chapter)
  4. longer projects:
    • version control: git
    • reproducable research
    • scandal!
franciscothebrady commented 4 years ago

very preliminary

damianrt commented 3 years ago

Possible Topics or sections

Carry out various types of data analysis: qualitative, quantitative

Data Structures as they relate to social science concepts

Statistical tests: t, Chisq, Anova

Modelling: Regression, lm, Glm, Fitting arbitrary models, Optim

The Formal empirical research process:

  1. Ask a question
  2. Formulate a hypothesis. Make a prediction based on assumed relationships between measurable concepts, aka theory.
  3. Collect data
  4. Analyze data
  5. Evaluate Hypothesis and refine theory if necessary
franciscothebrady commented 3 years ago

Some people to shamelessly steal from:

franciscothebrady commented 3 years ago

Data Acquisition (Working with APIs): https://data.library.virginia.edu/using-census-data-api-with-r/

franciscothebrady commented 3 years ago

@damianrt just wrote the git addendum

franciscothebrady commented 3 years ago

https://mgimond.github.io/Spatial/reading-and-writing-spatial-data-in-r.html

damianrt commented 3 years ago

In addition to the data structures R uses to store data, we should also cover the various types of variables from a data analysis perspective: numerical vs categorical, continuous vs discrete, ordered and unordered.

franciscothebrady commented 3 years ago

I think variable types comes before data structures right ?

damianrt commented 3 years ago

I agree. I think it is useful to introduce vectors and the data types before introducing data frames etc. if we want to be able to introduce programming or more advanced usage later.